Ant Based Semi-supervised Classification

  • Anindya Halder
  • Susmita Ghosh
  • Ashish Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)


Semi-supervised classification methods make use of the large amounts of relatively inexpensive available unlabeled data along with the small amount of labeled data to improve the accuracy of the classification. This article presents a novel ‘self-training’ based semi-supervised classification algorithm using the property of aggregation pheromone found in natural behavior of real ants. The proposed algorithm is evaluated with real life benchmark data sets in terms of classification accuracy. Also the method is compared with two conventional supervised classification methods and two recent semi-supervised classification techniques. Experimental results show the potentiality of the proposed algorithm.


Support Vector Machine Unlabeled Data Multi Layer Perceptron Aggregation Pheromone Transductive Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anindya Halder
    • 1
  • Susmita Ghosh
    • 2
  • Ashish Ghosh
    • 1
  1. 1.Indian Statistical InstituteCenter for Soft Computing ResearchKolkataIndia
  2. 2.Dept. of Computer Science & Engg.Jadavpur UniversityKolkataIndia

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